MiniMax M2.1: Enhanced Coding Toolkit Released

Overview

Just months after releasing M2—a fast, low-cost model designed for agents and code—MiniMax has introduced an enhanced version: MiniMax M2.1.

M2 already stood out for its efficiency, running at roughly 8% of the cost of Claude Sonnet while delivering significantly higher speed. More importantly, it introduced a different computational and reasoning pattern, particularly in how the model structures and executes its thinking during complex code and tool-driven workflows.

M2.1 builds on this foundation, bringing tangible improvements across key areas: better code quality, smarter instruction following, cleaner reasoning, and stronger performance across multiple programming languages. These upgrades extend the original strengths of M2 while staying true to MiniMax’s vision of “Intelligence with Everyone.”

Strengthening the core capabilities of M2, M2.1 is no longer just about better coding—it also produces clearer, more structured outputs across conversations, documentation, and writing.

Core Capabilities and Benchmark Results

• Built for real-world coding and AI-native teams: Designed to support everything from rapid “vibe builds” to complex, production-grade workflows.
• Goes beyond coding: Produces clearer, more structured, and higher-quality outputs across everyday conversations, technical documentation, and writing tasks.
• State-of-the-art multilingual coding performance: Achieves 72.5% on SWE-Multilingual, outperforming Claude Sonnet 4.5 and Gemini 3 Pro across multiple programming languages.
• Strong AppDev & WebDev capabilities: Scores 88.6% on VIBE-Bench, exceeding Claude Sonnet 4.5 and Gemini 3 Pro, with major improvements in native Android, iOS, and modern web development.
• Excellent agent and tool compatibility: Delivers consistent and stable performance across leading coding tools and agent frameworks, including Claude Code, Droid (Factory AI), Cline, Kilo Code, Roo Code, BlackBox, and more.
• Robust context management support: Works reliably with advanced context mechanisms such as Skill.md, Claude.md / agent.md / cursorrule, and Slash Commands, enabling scalable agent workflows.
• Automatic caching, zero configuration: Built-in caching works out of the box to reduce latency, lower costs, and deliver a smoother overall experience.

Getting Started with MiniMax M2.1

To get started with MiniMax M2.1, you’ll need an API key from the MiniMax platform. You can generate one from the MiniMax user console.

Installing & Setting up the Dependencies

MiniMax supports both the Anthropic and OpenAI API formats, making it easy to integrate MiniMax models into existing workflows with minimal configuration changes—whether you’re using Anthropic-style message APIs or OpenAI-compatible setups.

pip install anthropic

import os
from getpass import getpass
os.environ['ANTHROPIC_BASE_URL'] = 'https://api.minimax.io/anthropic'
os.environ['ANTHROPIC_API_KEY'] = getpass('Enter MiniMax API Key: ')

With just this minimal setup, you’re ready to start using the model.

Sending Requests to the Model

MiniMax M2.1 returns structured outputs that separate internal reasoning (thinking) from the final response (text). This allows you to observe how the model interprets intent and plans its answer before producing the user-facing output.

import anthropic
 
client = anthropic.Anthropic()
 
message = client.messages.create(
    model="MiniMax-M2.1",
    max_tokens=1000,
    system="You are a helpful assistant.",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Hi, how are you?"
                }
            ]
        }
    ]
)
 
for block in message.content:
    if block.type == "thinking":
        print(f"Thinking:\n{block.thinking}\n")
    elif block.type == "text":
        print(f"Text:\n{block.text}\n")

What makes MiniMax stand out is the visibility into its reasoning process. By cleanly separating reasoning from responses, the model becomes easier to interpret, debug, and trust, especially in complex agent-based or multi-step workflows. This clarity is paired with faster responses, more concise reasoning, and substantially reduced token consumption compared to M2.

Testing the Model’s Coding Capabilities

MiniMax M2 stands out for its native mastery of Interleaved Thinking, allowing it to dynamically plan and adapt within complex coding and tool-based workflows. To evaluate these capabilities, we’ll test using a structured coding prompt that includes multiple constraints and real-world engineering requirements.

import anthropic
 
client = anthropic.Anthropic()
 
def run_test(prompt: str, title: str):
    print(f"\n{'='*80}")
    print(f"TEST: {title}")
    print(f"{'='*80}\n")
 
    message = client.messages.create(
        model="MiniMax-M2.1",
        max_tokens=10000,
        system=(
            "You are a senior software engineer. "
            "Write production-quality code with clear structure, "
            "explicit assumptions, and minimal but sufficient reasoning. "
            "Avoid unnecessary verbosity."
        ),
        messages=[
            {
                "role": "user",
                "content": [{"type": "text", "text": prompt}]
            }
        ]
    )
 
    for block in message.content:
        if block.type == "thinking":
            print(f"Thinking:\n{block.thinking}\n")
        elif block.type == "text":
            print(f"Text:\n{block.text}\n")
 
PROMPT= """
Design a small Python service that processes user events.
 
Requirements:
1. Events arrive as dictionaries with keys: user_id, event_type, timestamp.
2. Validate input strictly (types + required keys).
3. Aggregate events per user in memory.
4. Expose two functions:
   - ingest_event(event: dict) -> None
   - get_user_summary(user_id: str) -> dict
5. Code must be:
   - Testable
   - Thread-safe
   - Easily extensible for new event types
6. Do NOT use external libraries.
 
Provide:
- Code only
- Brief inline comments where needed
""" 
 
run_test(prompt=PROMPT, title="Instruction Following + Architecture")

This test uses a structured prompt designed to evaluate the model's ability to handle complex coding requirements while providing clear design decisions.

Model Reasoning & Output

The model reasons through architectural trade-offs before coding, balancing flexibility, memory usage, and extensibility.

Model’s Interleaved Thinking in Action

MiniMax M2.1 can dynamically adjust its output based on interactions with external tools.

Defining the Tools

import anthropic
import json
 
client = anthropic.Anthropic()
 
def get_stock_metrics(ticker):
    data = {
        "NVDA": {"price": 130, "pe": 75.2},
        "AMD": {"price": 150, "pe": 40.5}
    }
    return json.dumps(data.get(ticker, "Ticker not found"))
    
def get_sentiment_analysis(company_name):
    sentiments = {"NVIDIA": 0.85, "AMD": 0.42}
    return f"Sentiment score for {company_name}: {sentiments.get(company_name, 0.0)}"
 
...

During execution, the model integrates tool outputs into its reasoning and adjusts its final comparison accordingly, showcasing its ability to interleave reasoning and tool usage.

Comparison with OpenAI’s GPT-5.2

When comparing outputs, MiniMax M2.1 produces a broader set of terms than GPT-5.2, which highlights its stronger adherence to instructions and deeper semantic coverage.

Conclusion

This all points to MiniMax M2.1's enhanced capabilities in structured coding, reasoning, and real-world applications, making it a significant advancement in AI-assisted coding tools.